Introduction

Ground Truth Solutions analysed survey data collected by the Argentinian Red Cross (ARCS) in early 2023, covering questions around trust as part of the Trust Index project.

Sampling and Coverage

The sampling employed a convenient sampling approach focused on large parts of the country in Argentina. The coverage overall is 96% of the country, when looking at inhabitants at the province level.

According to the provided information, the survey was mainly conducted at ARCS-organised events, which can be seen in the GPS coordinates, which indicate many local clusters and focused mainly on cities.

Overall, such sampling approach ensures familiarity with the ARCS, but also comes with the danger of biasing results, in particular social-desirability bias.

Except for seven of the 23 provinces, all the provinces of Argentina were covered in the sample. Since the seven that were not included are not highly populated, 96% of the Argentinian population lived in the surveyed provinces in 2022, which is a very high coverage, in particular also compared to the data collected in Zambia. Besides some deviations, the sample was roughly allocated proportionally the population of the respective province.

Demographic data

For the following demographic data, we show the full data set including people who indicated to volunteered for the ARCS as well as those who benefited from the ARCS.

Age and gender

Compared to the overall Argentinean population, in the sample women, especially younger women are over-represented, while elderly people are under-represented.

Education

While we have not managed to access to demographic data on education using the same levels as the survey, the survey demographics on education at first glance seems to correspond to expected values of the various levels of education in the overall population, with the exception of percentages of complete and uncompleted university degrees. As we will see in the employment questions, full time students with approximately 10% are over-represented in the survey population, which in the overall Argentinian population above 18 was close to 6% in 2020. Our weighting procedure will correct this large share of students when calculating the weighted overall results.

Employment

The sample’s demographic composition on employments status seems to indicate that people interviewed have a much higher rate of unemployment than the average of the country, which had an unemployment rate of 6.5% in 2022 and labor force participation rate of 60% in the same year.

Distribution of mean scores for values and competencies per demographic questions

When looking at the average raw scores for all the competencies and value question, we can produce a number of breakdowns using the presented demographic variables.

Competencies

Values

Upon analyzing the data, we observe that there are not significant variations in the results based on age, gender, previous beneficiary status or volunteer status. The largest variations we see for the location variable. The province where the survey was conducted plays the largest role in contributing to the variation of the results. However, since the sample allocation per province was done almost proportionally, we do not expect large changes once the weighting has been implemented.

Weighting

To address the deviation of demographic parameters from the overall population, we have utilized a technique called raking. The raking process adjusts the results based on several variables to ensure that our sample reflects the distribution of these variables in the overall population. Here are the variables we considered for raking:

Using an appropriate package in R to conduct the raking, we obtained the following results.

While the weighted results have smaller means, just as the weighted results in Zambia, the difference between weighted and un-weighted results are fairly minor in comparison to the Zambia data.

Unweighted Results

Weighted Results

Here we are presenting the weighted data obtained through the raking process, which takes into account variables such as gender, age groups, province, full-time student status and volunteer status. It is important to note that this data can be biased due to sampling done at ARCS-organised events in urban areas. Hence it should be considered indicative rather than representative of the entire country. However, overall data quality and reliability seems much higher than from data collected in Zambia

Results per beneficiaries, volunteers and others

Beneficiaries Volunteers Others
Respondents 736 560 1940